A novel noise reduction algorithm and its application in failure information extraction of bearing

The performance of rolling bearing can directly affect the reliability of a rotating machine. Due to the influence of noise and irrelevant components, the fault information is rather weak in a bearing fault, which adds to the difficulty in judgment of bearing state and identification of fault types. To solve the problem of heterogeneous feature information caused by noise jamming in rolling bearing failure, difficulty in extracting fault feature, and misdiagnosis and misjudgment, the paper has proposed a denoising algorithm according to the covariance matrix of a Hankel matrix and information entropy (Hankel-Cov-IE). First, covariance matrix of a Hankel matrix of signals is constructed, and fault signals are preliminarily denoised based on denoising capacity of the covariance matrix and its nature of combining global and local features. To address the selection of sensitive fault components, information entropy (IE) is taken advantage of to screen out the vectors containing abundant feature information from the covariance matrix. The filtered feature vectors are reconstructed in the way of equal weight and faults of rolling bearing are identified with the spectrum of signals reconstructed. The presented algorithm is compared with different typical methods. The results indicate that the presented algorithm can reduce noise more effectively and achieve extraction of bearing failure features more comprehensively and correctly, with correct judgment of fault types. The presented algorithm can correctly extract the feature frequency of bearing fault and recognize the types of faults, and is insensitive to sensor sites, speeds, and failure types of bearings.